WEBVTT 00:00:09.250 --> 00:00:11.560 Our discussion about the future of the workforce 00:00:11.560 --> 00:00:14.670 would not be complete without an exploration of what impact 00:00:14.670 --> 00:00:17.060 the current innovations in technology will have, 00:00:17.060 --> 00:00:19.780 specifically, artificial intelligence. 00:00:19.780 --> 00:00:21.840 Over the past three years, there have 00:00:21.840 --> 00:00:24.150 been significant innovations in big data, 00:00:24.150 --> 00:00:27.270 database architecture, and artificial intelligence, 00:00:27.270 --> 00:00:30.730 that are enabling new business models and products. 00:00:30.730 --> 00:00:33.070 In simplest terms, the innovations 00:00:33.070 --> 00:00:35.900 in artificial intelligence are equipping algorithms 00:00:35.900 --> 00:00:39.670 to make smarter decisions about tasks and problems 00:00:39.670 --> 00:00:43.520 that have, so far, been thought only to be reserved for humans. 00:00:43.520 --> 00:00:47.230 The accompanying innovations, and cheapening of hardware, 00:00:47.230 --> 00:00:50.310 that enables artificial intelligence algorithms to read 00:00:50.310 --> 00:00:52.910 and process incredibly large data sets, 00:00:52.910 --> 00:00:55.550 has motivated more entrepreneurs, 00:00:55.550 --> 00:00:58.830 and technologists, to innovate with artificial intelligence. 00:00:58.830 --> 00:01:01.570 Many think that the implications for the workplace 00:01:01.570 --> 00:01:03.010 will hurt workers. 00:01:03.010 --> 00:01:05.630 Similar to how the Industrial Revolution displaced 00:01:05.630 --> 00:01:07.780 many workers through the creation of tech 00:01:07.780 --> 00:01:11.180 that was cheaper and overall more efficient than people. 00:01:11.180 --> 00:01:14.250 People believe that AI will replace, not 00:01:14.250 --> 00:01:17.700 just blue collar workers, but also white collar workers. 00:01:17.700 --> 00:01:21.190 However, the fact is that the impact on workers 00:01:21.190 --> 00:01:24.470 is under our control, the control of entrepreneurs, 00:01:24.470 --> 00:01:26.990 technologists, and everyday citizens, 00:01:26.990 --> 00:01:28.880 that are part of the workforce. 00:01:28.880 --> 00:01:31.440 Business owners and workers can harness 00:01:31.440 --> 00:01:34.670 AI to augment human intelligence in the workplace 00:01:34.670 --> 00:01:39.630 to creatively improve decision making instead of replacing it. 00:01:39.630 --> 00:01:44.290 Here's Rob High, the CTO of IBM Watson, one of the primary R&D 00:01:44.290 --> 00:01:48.930 leaders in the AI space, making this exact point at the 2016 00:01:48.930 --> 00:01:52.220 MIT Technology Conference. 00:01:52.220 --> 00:01:54.210 If we become experts in everything, 00:01:54.210 --> 00:01:55.540 we're experts and nothing. 00:01:55.540 --> 00:01:57.170 And so the same phenomenon occurs 00:01:57.170 --> 00:01:58.860 within these cognitive systems as well. 00:01:58.860 --> 00:02:01.230 They can actually begin to get a little bit confused 00:02:01.230 --> 00:02:03.340 in how to ration-- 00:02:03.340 --> 00:02:04.740 this would be a good question. 00:02:04.740 --> 00:02:06.840 So a couple points to be made about that. 00:02:06.840 --> 00:02:10.150 That said, because each of these are distinct, 00:02:10.150 --> 00:02:12.210 we can actually set the system up 00:02:12.210 --> 00:02:15.397 based on whatever is appropriate for the application we're 00:02:15.397 --> 00:02:16.230 trying to solve for. 00:02:16.230 --> 00:02:19.470 For the case of oncology treatment advice, 00:02:19.470 --> 00:02:21.390 the goal is actually about trying 00:02:21.390 --> 00:02:24.510 to identify the best treatment based on outcomes, based 00:02:24.510 --> 00:02:27.691 on standard of care practices and clinical expertise, 00:02:27.691 --> 00:02:29.940 based on similarity of this patient to other patients. 00:02:29.940 --> 00:02:31.900 And all those can contribute to help 00:02:31.900 --> 00:02:35.560 Watson come back with an ordered list of potential treatments. 00:02:35.560 --> 00:02:37.560 But again, we're not trying to make 00:02:37.560 --> 00:02:38.770 the decision for the human. 00:02:38.770 --> 00:02:40.616 We're not trying to think for the human. 00:02:40.616 --> 00:02:42.990 What we're trying to do is do the research for the human, 00:02:42.990 --> 00:02:45.410 so the human can then go make the decision better. 00:02:45.410 --> 00:02:46.880 In this case the doctor. 00:02:46.880 --> 00:02:48.930 So we're not going take-- 00:02:48.930 --> 00:02:51.180 Watson doesn't make the decision about what treatment 00:02:51.180 --> 00:02:52.850 to give for the doctor. 00:02:52.850 --> 00:02:55.840 It presents the doctor a set of relevant treatments that 00:02:55.840 --> 00:02:59.250 has been rationalized based on all this other information, 00:02:59.250 --> 00:03:01.910 at a speed that a doctor could not do on their own, 00:03:01.910 --> 00:03:03.870 including finding all the literature that's 00:03:03.870 --> 00:03:05.661 relevant to support why they should believe 00:03:05.661 --> 00:03:11.720 in that treatment or not. 00:03:11.720 --> 00:03:14.550 So what specific type of disruptions 00:03:14.550 --> 00:03:18.060 can we expect AI to cause for employers and employees? 00:03:18.060 --> 00:03:21.470 Let's take a look at what David Autor, a professor of economics 00:03:21.470 --> 00:03:23.900 at MIT, said in the Future of Work panel 00:03:23.900 --> 00:03:26.120 at the Nobel Week Dialogue of 2015. 00:03:26.120 --> 00:03:30.310 In terms of employment, as was said earlier, 00:03:30.310 --> 00:03:32.030 people have been worried for centuries 00:03:32.030 --> 00:03:33.610 about displacement of labor. 00:03:33.610 --> 00:03:35.380 And labor has been vastly displaced. 00:03:35.380 --> 00:03:38.610 In the start of the 20th century, 40% of US employment 00:03:38.610 --> 00:03:40.030 was on farms. 00:03:40.030 --> 00:03:41.280 Now it's under 2%. 00:03:41.280 --> 00:03:43.740 We had no idea what was coming. 00:03:43.740 --> 00:03:44.710 But it came. 00:03:44.710 --> 00:03:46.620 We find uses for ourselves. 00:03:46.620 --> 00:03:49.870 And that's partly because the technology augments us, 00:03:49.870 --> 00:03:51.480 it doesn't just replace us. 00:03:51.480 --> 00:03:54.380 It makes the rest of what we do more valuable. 00:03:54.380 --> 00:03:57.390 So, you know, I can make more as an Uber driver 00:03:57.390 --> 00:03:59.340 than as a rickshaw driver, not just 00:03:59.340 --> 00:04:02.970 because I'm in a rich world, but because I have a tool that 00:04:02.970 --> 00:04:05.096 makes me able to transport people much faster 00:04:05.096 --> 00:04:07.220 and further, and more safely, and more comfortably, 00:04:07.220 --> 00:04:08.470 in a given amount of time. 00:04:08.470 --> 00:04:10.990 In many, many ways, the tools we make 00:04:10.990 --> 00:04:12.322 make our time more valuable. 00:04:12.322 --> 00:04:14.280 Because there's always some piece that we still 00:04:14.280 --> 00:04:17.760 have to supply, and because that becomes a scarce factor 00:04:17.760 --> 00:04:19.640 that raises our labor value. 00:04:19.640 --> 00:04:21.089 So there's a challenge-- 00:04:21.089 --> 00:04:23.410 So now let me say, but let's say I'm wrong, 00:04:23.410 --> 00:04:25.897 and that we're going to be all replaced by robots. 00:04:25.897 --> 00:04:27.480 That they can do all the work we want. 00:04:27.480 --> 00:04:28.960 Is that a problem? 00:04:28.960 --> 00:04:31.330 Well, if it's a problem, it's a very unusual 00:04:31.330 --> 00:04:32.250 historical problem. 00:04:32.250 --> 00:04:34.870 Most problems, economic problems, societal problems, 00:04:34.870 --> 00:04:36.710 are problems of scarcity, of not having 00:04:36.710 --> 00:04:39.196 enough of something, enough food, enough power, 00:04:39.196 --> 00:04:40.445 enough safety, enough shelter. 00:04:45.020 --> 00:04:47.660 In addition to changing the kinds of roles 00:04:47.660 --> 00:04:49.900 that workers can take on in the workplace, 00:04:49.900 --> 00:04:53.270 AI-based innovation can also impact social inequality, 00:04:53.270 --> 00:04:55.410 for better or worse, to be determined 00:04:55.410 --> 00:04:57.120 by how we choose to behave. 00:04:57.120 --> 00:05:00.030 For example, women and people of color 00:05:00.030 --> 00:05:03.490 are very under-represented among technology entrepreneurs that 00:05:03.490 --> 00:05:05.310 receive funding from venture capitalists 00:05:05.310 --> 00:05:06.960 for their enterprises. 00:05:06.960 --> 00:05:10.420 If venture capital investment goes towards a more diverse set 00:05:10.420 --> 00:05:13.310 of entrepreneurs, then we will see social inequality 00:05:13.310 --> 00:05:14.750 improve positively. 00:05:14.750 --> 00:05:18.060 Here's Amanda Kahlow, CEO of 6Sense, 00:05:18.060 --> 00:05:20.520 a predictive intelligence platform for marketing 00:05:20.520 --> 00:05:23.720 and sales, expanding on the current under-representation, 00:05:23.720 --> 00:05:25.970 based on her experiences. 00:05:25.970 --> 00:05:28.300 The short answer is yes, I feel supported. 00:05:28.300 --> 00:05:32.310 But I do feel-- so I go to a lot of CEO events 00:05:32.310 --> 00:05:33.977 and sit on a panel like this. 00:05:33.977 --> 00:05:36.060 The last, I was in Hawaii just a little while ago. 00:05:36.060 --> 00:05:38.460 250 CEOs, I was the only woman. 00:05:38.460 --> 00:05:41.750 And in enterprise tech, I'm less than 1% in this world. 00:05:41.750 --> 00:05:42.800 And it's crazy. 00:05:42.800 --> 00:05:44.327 And it doesn't feel right. 00:05:44.327 --> 00:05:45.910 And I think it's because we're missing 00:05:45.910 --> 00:05:48.243 that confidence, and the passion to go forward, and want 00:05:48.243 --> 00:05:49.560 to dream big. 00:05:49.560 --> 00:05:50.896 Do I feel supported by the men? 00:05:50.896 --> 00:05:52.020 I think yes, I'm supported. 00:05:52.020 --> 00:05:54.260 I think pretty much, I feel very supported. 00:05:54.260 --> 00:05:57.170 Especially my male co-founders are ridiculously supportive. 00:05:57.170 --> 00:05:58.670 And actually, I think that they have 00:05:58.670 --> 00:06:00.970 more faith and confidence in me than I do myself, 00:06:00.970 --> 00:06:02.010 which is amazing. 00:06:02.010 --> 00:06:05.570 I think I have had to, maybe, guide 00:06:05.570 --> 00:06:07.630 and teach some of my VCs and other people 00:06:07.630 --> 00:06:09.340 in the world what's important. 00:06:09.340 --> 00:06:11.195 And it's not always just about-- 00:06:11.195 --> 00:06:13.070 I think we were talking earlier about the end 00:06:13.070 --> 00:06:15.070 goal of the business, and growing the business-- 00:06:15.070 --> 00:06:16.720 but creating a sustainable company 00:06:16.720 --> 00:06:18.550 is also about the culture and the people. 00:06:18.550 --> 00:06:22.330 And I think that what we're missing in today's workforce 00:06:22.330 --> 00:06:24.380 is putting an emphasis on that. 00:06:24.380 --> 00:06:27.280 In addition to all of the great technologies that we can do. 00:06:27.280 --> 00:06:30.090 And that's the thing that the women bring to the table. 00:06:30.090 --> 00:06:33.060 I'm super proud of our lack of attrition rate at 6Sense. 00:06:33.060 --> 00:06:34.560 We don't have people leave, and it's 00:06:34.560 --> 00:06:36.900 because they feel supported and heard, 00:06:36.900 --> 00:06:39.540 and they can also be creative and do amazing things 00:06:39.540 --> 00:06:41.640 in the work that we're providing. 00:06:41.640 --> 00:06:45.435 But I think it's not intentional when men aren't supporting. 00:06:45.435 --> 00:06:46.810 I was actually sitting on a plane 00:06:46.810 --> 00:06:49.245 the other day talking to a gentleman. 00:06:49.245 --> 00:06:50.370 And he asked me what I did. 00:06:50.370 --> 00:06:52.640 And I never come out and say, I'm the CEO of this company. 00:06:52.640 --> 00:06:54.110 I say I work for a software company. 00:06:54.110 --> 00:06:56.380 And I started talking about the stuff that we're doing. 00:06:56.380 --> 00:06:58.190 And then two hours into the conversation, he's like, well, 00:06:58.190 --> 00:06:58.900 what do you do there? 00:06:58.900 --> 00:06:59.910 And he's like, are you in marketing? 00:06:59.910 --> 00:07:01.370 And I was like, no, I'm the CEO. 00:07:01.370 --> 00:07:04.794 And he got up from his seat, and he had this visceral reaction. 00:07:04.794 --> 00:07:05.710 And he didn't mean to. 00:07:05.710 --> 00:07:06.310 And then he sat down. 00:07:06.310 --> 00:07:08.726 He's like, I'm so sorry, I didn't mean to react like that. 00:07:08.726 --> 00:07:10.300 And [? he ?] didn't intend to-- 00:07:10.300 --> 00:07:11.010 [LAUGHTER] 00:07:11.010 --> 00:07:15.990 It was just a lack of filter which we all have sometimes. 00:07:15.990 --> 00:07:18.990 When he was realizing who I was in the company-- and I 00:07:18.990 --> 00:07:21.080 wear braids and pigtails, and I don't always 00:07:21.080 --> 00:07:23.440 dress the part of something else. 00:07:23.440 --> 00:07:25.700 I always struggle with being my authentic woman self. 00:07:25.700 --> 00:07:29.760 I want to maintain that while dominating in the male world 00:07:29.760 --> 00:07:30.260 that I'm in. 00:07:30.260 --> 00:07:32.750 So I think there are levels of education 00:07:32.750 --> 00:07:33.750 that we can give others. 00:07:33.750 --> 00:07:35.070 Thank you. 00:07:35.070 --> 00:07:37.620 Income and social inequality between genders 00:07:37.620 --> 00:07:39.380 will not improve unless we choose 00:07:39.380 --> 00:07:41.620 to make fairer decisions about who 00:07:41.620 --> 00:07:43.420 gets opportunities to create and run 00:07:43.420 --> 00:07:45.620 companies in the tech industry. 00:07:45.620 --> 00:07:48.860 If we do not actively monitor our biases, 00:07:48.860 --> 00:07:50.880 we will not be able to take advantage 00:07:50.880 --> 00:07:53.500 of this period of innovation and wealth creation 00:07:53.500 --> 00:07:55.460 towards a more equitable future. 00:07:55.460 --> 00:07:57.880 There is also an opportunity gap among people 00:07:57.880 --> 00:08:01.330 for who gets to work within companies in the tech industry. 00:08:01.330 --> 00:08:05.820 Leila Janah, founder and CEO of Sama Group, an organization 00:08:05.820 --> 00:08:09.350 that lifts people out of poverty by enabling them to do work 00:08:09.350 --> 00:08:12.690 in the digital economy, discusses this opportunity gap, 00:08:12.690 --> 00:08:14.680 and how we can mitigate it. 00:08:14.680 --> 00:08:17.400 Now interestingly, traditionally, 00:08:17.400 --> 00:08:19.580 in the informal sector, if you're doing, 00:08:19.580 --> 00:08:21.950 say construction work, and you go 00:08:21.950 --> 00:08:23.590 and you find your job by standing 00:08:23.590 --> 00:08:26.060 on the side of the road and waiting for someone to hire you 00:08:26.060 --> 00:08:29.070 as a labor contractor, there is no such thing 00:08:29.070 --> 00:08:31.120 as getting rewarded for good work. 00:08:31.120 --> 00:08:33.760 You might put in a wonderful 10 hour shift, 00:08:33.760 --> 00:08:36.809 but you will get no five star rating on a website. 00:08:36.809 --> 00:08:38.542 You go back to zero the next day, 00:08:38.542 --> 00:08:41.000 standing on the side of the road waiting for your next job. 00:08:41.000 --> 00:08:44.010 So I think that technology actually 00:08:44.010 --> 00:08:47.950 brings a lot more transparency in areas like wages, 00:08:47.950 --> 00:08:51.060 and reviews, and feedback, that are very good for low income 00:08:51.060 --> 00:08:51.910 workers. 00:08:51.910 --> 00:08:53.610 And I'll say one last thing on this, 00:08:53.610 --> 00:08:56.200 which is that, David and I were talking about this just 00:08:56.200 --> 00:08:58.720 before the panel, we so often assume 00:08:58.720 --> 00:09:03.290 that technology has some kind of morality built into it. 00:09:03.290 --> 00:09:05.420 Technology, in fact, is agnostic. 00:09:05.420 --> 00:09:08.070 It's as agnostic as roads, and waterways. 00:09:08.070 --> 00:09:10.340 It's all about what we do with technology. 00:09:10.340 --> 00:09:14.030 We can choose to inject morality into the technology we build. 00:09:14.030 --> 00:09:18.020 We can choose to build systems that enfranchise the poor. 00:09:18.020 --> 00:09:19.570 Or we can choose not to. 00:09:19.570 --> 00:09:22.610 But we're not victims of technological progress. 00:09:22.610 --> 00:09:26.170 It's not happening in a way that we can't control. 00:09:26.170 --> 00:09:28.510 At least until the singularity happens, at which point 00:09:28.510 --> 00:09:29.820 all bets are off. 00:09:29.820 --> 00:09:30.700 [LAUGHTER] 00:09:30.700 --> 00:09:33.680 The first is, job training needs to completely change. 00:09:33.680 --> 00:09:37.880 In the US, which is the best example I know, it's abysmal. 00:09:37.880 --> 00:09:41.350 We are still training people for jobs that are disappearing 00:09:41.350 --> 00:09:42.670 at an alarming rate. 00:09:42.670 --> 00:09:45.289 We are not training them in how to be entrepreneurial 00:09:45.289 --> 00:09:46.330 and marketing themselves. 00:09:46.330 --> 00:09:48.770 The skill that you need for 21st century jobs 00:09:48.770 --> 00:09:50.220 is marketing yourself. 00:09:50.220 --> 00:09:52.620 If Kim Kardashian can teach us anything, 00:09:52.620 --> 00:09:54.600 it's probably only that. 00:09:54.600 --> 00:09:57.070 But interestingly, all of these new platforms 00:09:57.070 --> 00:09:59.250 require you to be able to set up a profile, 00:09:59.250 --> 00:10:01.690 to choose the right kind of a photograph that will attract 00:10:01.690 --> 00:10:03.687 customers, to send a follow up email, 00:10:03.687 --> 00:10:05.020 to have customer service skills. 00:10:05.020 --> 00:10:08.100 And you're absolutely right, that many low income people-- 00:10:08.100 --> 00:10:11.130 And just to give you an idea of how profound this is, in the US 00:10:11.130 --> 00:10:13.100 we have one of our centers which is 00:10:13.100 --> 00:10:15.590 less than a mile from the Facebook headquarters 00:10:15.590 --> 00:10:17.790 in Silicon Valley, in East Palo Alto. 00:10:17.790 --> 00:10:19.430 Which is a very poor community. 00:10:19.430 --> 00:10:24.080 And fully 25% of our incoming class for one of our trainings 00:10:24.080 --> 00:10:28.250 last year had people who had zero internet access at home, 00:10:28.250 --> 00:10:30.420 and no smartphone. 00:10:30.420 --> 00:10:32.860 A mile from the Facebook campus. 00:10:32.860 --> 00:10:35.200 So we can talk all we want about how technology is going 00:10:35.200 --> 00:10:37.100 to make everyone's lives better, but the reality 00:10:37.100 --> 00:10:38.710 is that many people are disconnected. 00:10:38.710 --> 00:10:41.600 And if you didn't grow up having the internet at home, how can 00:10:41.600 --> 00:10:44.740 you possibly understand how to market yourself? 00:10:44.740 --> 00:10:47.310 It's not easy to be an autodidact, 00:10:47.310 --> 00:10:49.840 and to learn how to teach yourself things on YouTube, 00:10:49.840 --> 00:10:51.506 if you didn't grow up with the internet. 00:10:55.470 --> 00:10:57.610 Advancements in technology taking place right 00:10:57.610 --> 00:10:59.390 now are incredibly exciting. 00:10:59.390 --> 00:11:02.160 And we all stand to gain from them as consumers. 00:11:02.160 --> 00:11:05.680 However, as with other periods of societal change, 00:11:05.680 --> 00:11:08.350 we, citizens and the government, have 00:11:08.350 --> 00:11:11.490 to ensure that the opportunities to launch, run, and work, 00:11:11.490 --> 00:11:14.610 in these new enterprises is made available to all members 00:11:14.610 --> 00:11:16.420 of society.